Vision-Enhanced Large Language Models for High-Resolution Image Synthesis and Multimodal Data Interpretation

• URL: http://arxiv.org/abs/2512.12595v1
• Date: Sun, 14 Dec 2025 08:28:50 GMT
• Title: Vision-Enhanced Large Language Models for High-Resolution Image Synthesis and Multimodal Data Interpretation
• Authors: Karthikeya KV,
• Abstract summary: This research introduces a transformative framework for integrating Vision-Enhanced Large Language Models (LLMs) with advanced transformer-based architectures.<n>The proposed model incorporates a rectified flow mechanism that connects noise and data with linear paths, enabling efficient and high-quality generation.<n>The framework achieves unparalleled fidelity in synthesized images and coherent multimodal representations.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: This research introduces a transformative framework for integrating Vision-Enhanced Large Language Models (LLMs) with advanced transformer-based architectures to tackle challenges in high-resolution image synthesis and multimodal data interpretation. The proposed model incorporates a rectified flow mechanism that connects noise and data with linear paths, enabling efficient and high-quality generation. A bidirectional tokenization strategy is employed to seamlessly merge inputs from text, image, and video modalities, fostering a unified understanding across diverse data types. By embedding spatial-temporal features and leveraging a hybrid text-image sequence modeling approach, the framework achieves unparalleled fidelity in synthesized images and coherent multimodal representations. The architecture is optimized with a noise-aware learning algorithm, addressing discrepancies in noisy data distributions and improving generative performance under varying input conditions. Rigorous evaluations on benchmark datasets demonstrate a 25% increase in image resolution clarity and a 20% reduction in computational requirements compared to diffusion-based methods. Furthermore, the model exhibits robust scalability and adaptability, showcasing its potential in applications like autonomous systems, creative content generation, and advanced video analysis. This work underscores the role of vision-centric LLMs in redefining capabilities in computer vision and multimodal artificial intelligence.

Related papers

• Multimodal Interpretation of Remote Sensing Images: Dynamic Resolution Input Strategy and Multi-scale Vision-Language Alignment Mechanism [10.17375002962432]
  This study proposes a Vision-language Model (VLM) framework integrated with two key innovations.<n>The DRIS adopts a coarse-to-fine approach to adaptively allocate computational resources according to the complexity of image content.<n>The MS-VLAM constructs a three-tier alignment mechanism covering object, local-region and global levels.
  arXiv  Detail & Related papers  (2025-12-29T06:51:20Z)
• Exploring a Unified Vision-Centric Contrastive Alternatives on Multi-Modal Web Documents [99.62178668680578]
  We propose Vision-Centric Contrastive Learning (VC2L), a unified framework that models text, images, and their combinations using a single vision transformer.<n> VC2L operates entirely in pixel space by rendering all inputs, whether textual, visual, or combined, as images.<n>To capture complex cross-modal relationships in web documents, VC2L employs a snippet-level contrastive learning objective that aligns consecutive multimodal segments.
  arXiv  Detail & Related papers  (2025-10-21T14:59:29Z)
• Growing Visual Generative Capacity for Pre-Trained MLLMs [60.826355079902505]
  Bridge is a pure autoregressive unified MLLM that augments pre-trained visual understanding models with generative ability.<n>We propose a semantic-to-pixel discrete representation that integrates compact semantic tokens with fine-grained pixel tokens.
  arXiv  Detail & Related papers  (2025-10-02T00:40:02Z)
• Query-Kontext: An Unified Multimodal Model for Image Generation and Editing [53.765351127477224]
  Unified Multimodal Models (UMMs) have demonstrated remarkable performance in text-to-image generation (T2I) and editing (TI2I)<n>We introduce Query-Kontext, a novel approach that bridges the VLM and diffusion model via a multimodal kontext'' composed of semantic cues and coarse-grained image conditions encoded from multimodal inputs.<n> Experiments show that our approach matches strong unified baselines and even outperforms task-specific state-of-the-art methods in several cases.
  arXiv  Detail & Related papers  (2025-09-30T17:59:46Z)
• Multi-Modal Interpretability for Enhanced Localization in Vision-Language Models [2.984679075401059]
  This paper presents the Multi-Modal Explainable Learning framework, designed to enhance the interpretability of vision-language models.<n>Our approach processes features at multiple semantic levels to capture relationships between image regions at different granularities.<n>We show that by incorporating semantic relationship information into gradient-based attribution maps, MMEL produces more focused and contextually aware visualizations.
  arXiv  Detail & Related papers  (2025-09-17T18:18:59Z)
• Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation [54.588082888166504]
  We present Mogao, a unified framework that enables interleaved multi-modal generation through a causal approach.<n>Mogoo integrates a set of key technical improvements in architecture design, including a deep-fusion design, dual vision encoders, interleaved rotary position embeddings, and multi-modal classifier-free guidance.<n>Experiments show that Mogao achieves state-of-the-art performance in multi-modal understanding and text-to-image generation, but also excels in producing high-quality, coherent interleaved outputs.
  arXiv  Detail & Related papers  (2025-05-08T17:58:57Z)
• Multi-Head Attention Driven Dynamic Visual-Semantic Embedding for Enhanced Image-Text Matching [0.8611782340880084]
  This study proposes an innovative visual semantic embedding model, Multi-Headed Consensus-Aware Visual-Semantic Embedding (MH-CVSE)<n>This model introduces a multi-head self-attention mechanism based on the consensus-aware visual semantic embedding model (CVSE) to capture information in multiple subspaces in parallel.<n>In terms of loss function design, the MH-CVSE model adopts a dynamic weight adjustment strategy to dynamically adjust the weight according to the loss value itself.
  arXiv  Detail & Related papers  (2024-12-26T11:46:22Z)
• Unifying Visual and Semantic Feature Spaces with Diffusion Models for Enhanced Cross-Modal Alignment [20.902935570581207]
  We introduce a Multimodal Alignment and Reconstruction Network (MARNet) to enhance the model's resistance to visual noise. MARNet includes a cross-modal diffusion reconstruction module for smoothly and stably blending information across different domains. Experiments conducted on two benchmark datasets, Vireo-Food172 and Ingredient-101, demonstrate that MARNet effectively improves the quality of image information extracted by the model.
  arXiv  Detail & Related papers  (2024-07-26T16:30:18Z)
• UniDiff: Advancing Vision-Language Models with Generative and Discriminative Learning [86.91893533388628]
  This paper presents UniDiff, a unified multi-modal model that integrates image-text contrastive learning (ITC), text-conditioned image synthesis learning (IS), and reciprocal semantic consistency modeling (RSC) UniDiff demonstrates versatility in both multi-modal understanding and generative tasks.
  arXiv  Detail & Related papers  (2023-06-01T15:39:38Z)
• Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond [62.730497582218284]
  We develop a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation. We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data.
  arXiv  Detail & Related papers  (2020-04-30T03:23:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.